Method and apparatus for image reconstruction using data decomposition for all or portions of the processing flow

ABSTRACT

A method and apparatus for processing raw image data to create processed images. Raw image data is acquired. The raw image data is decomposed by a data decomposer into N subsets of raw image data. The number N is based on a number of available image generation processors. The N subsets of raw image data are processed by at least one image generation processor to create processed image data. If more than one image generation processor is available, the image generation processors perform image processing on the raw image data in parallel with respect to each other.

BACKGROUND OF THE INVENTION

This invention relates generally to processing and image reconstructionbased on acquired raw image data. In particular, the present inventionrelates to increasing the processing performance with respect to theimage reconstruction of diagnostic image data.

Raw image data from various diagnostic medical systems, such as ComputedTomography (CT) and Positron Emission Tomography (PET) systems, isacquired for diagnostic purposes. The CT and PET systems need to be ableto support numerous scanning and reconstruction modes. The associatedreconstruction algorithms are complex and computationally intensive.Users of the diagnostic medical systems desire an improvement in imagequality, along with minimizing the time required to generate imagesbased on raw image data and improving the reliability of thereconstruction process. By decreasing the amount of time needed togenerate the desired images from raw image data, images can be evaluatedsooner and patient through-put may be improved.

Previous diagnostic systems used different specialized processing unitsto accomplish specific tasks. That is, the reconstruction process wasbroken down according to the steps to be done. The processing units mayoperate in parallel or serially with respect to each other. In order toadd processing capability, however, new processing units had to be addedand the system design reconfigured and/or coordinated to integrate thenew units and steps, both increasing the complexity and limiting theflexibility of the diagnostic system. Scalability and increasedperformance are thus difficult to achieve when adding additionalprocessing units.

Thus, an apparatus and method are desired to reconstruct image data thataddresses the problems noted above and others previously experienced.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, a method for generating images. Raw image datarepresentative of an object of interest is acquired. The raw image datais decomposed into N subsets of raw image data. N is based on a numberof available image generation processors. The N subsets of raw imagedata are processed to create processed image data. The image generationprocessors perform image processing on the image data in parallel withrespect to each other.

In another embodiment, a method for increasing the performance of asystem for processing raw image data. Raw image data is acquired whichis representative of an object of interest. The raw image data isdivided into substantially equal subsets of raw image data. At least oneof the substantially equal subsets of raw image data is processed withan image generation processor.

In another embodiment, a scalable apparatus for processing raw imagedata. A data decomposer divides raw image data which is acquired by adata acquisition system. At least two image generation processorsprocess the raw image data in parallel with respect to each other.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computed tomography (CT) imaging system formed inaccordance with an embodiment of the present invention.

FIG. 2 illustrates a block diagram of the CT imaging system formed inaccordance with an embodiment of the present invention.

FIG. 3 illustrates a Positron Emission Tomography (PET) scanner systemformed in accordance with an embodiment of the present invention.

FIG. 4 illustrates a detector unit and associated PMT signals inaccordance with an embodiment of the present invention.

FIG. 5 illustrates a method for image reconstruction using datadecomposition in accordance with an embodiment of the present invention.

FIG. 6 illustrates a data decomposition and image generation processmodel formed in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a CT imaging system 10 formed in accordance with anembodiment of the present invention. The CT imaging system 10 includesan x-ray source 12 oriented to project a cone beam 14 of x-rays from afocal spot 16 (FIG. 2) through a patient 18 to be received by atwo-dimensional detector array 20. The two-dimensional detector array 20includes a number of detector elements 22 arranged over the area of thedetector array 20 in generally perpendicular columns and rows to detecta projected image of the x-rays 14 passing through the patient 18. Therows of detector elements 22 may extend along an in-slice dimension. Byway of example only, each row may include 1,000 separate detectorelements 22, and the array 20 may include 16 rows disposed along theslice dimension. The detectors 22 may be gas or solid state detectorswhich produce an electrical signal proportional to the x-ray fluxreceived over the sample period.

The x-ray source 12 and the two-dimensional detector array 20 aremounted on either side of a gantry 24 so as to rotate about an axis ofrotation 26 generally positioned within the patient 18. The axis ofrotation 26 forms the z-axis of a Cartesian coordinate system having itsorigin centered within the cone beam 14. The plane defined by the x andy axis of this coordinate system thus defines a plane of rotation,specifically the gantry plane 28 of the gantry 24.

FIG. 2 illustrates a block diagram of the CT imaging system 10 formed inaccordance with an embodiment of the present invention. The controlsubsystem of the CT imaging system 10 has gantry associated controlmodules 30 which include: x-ray controller 32, which provides power andtiming signals to the x-ray source 12, gantry motor controller 34, whichcontrols the rotational speed and position of the gantry 24. A dataacquisition system (DAS) 36 receives raw image data from thetwo-dimensional detector array 20 and converts the data into digitalform for later computer processing. The x-ray controller 32, the gantrymotor controller 34 and the data acquisition system 36 are connected tocomputer 38. The computer 38 also governs operation of a table motorcontrol 37 which drives a motor that moves the patient table 39 alongthe z-axis 26.

The computer 38 is a general purpose minicomputer programmed to acquireand manipulate projection data. The computer 38 is connected to a datadecomposer 46 and one or more image generation (IG) processors 40. Thedata decomposer 46 sends raw image data to the IG processors 40 whichprocess raw image data as discussed below. In FIG. 2, three IGprocessors 40 are illustrated, although it should be understood thatmore or less than three IG processors 40 may be utilized by the CTimaging system 10.

The computer 38 receives commands and scanning parameters via operatorconsole 42 which is generally a CRT display and keyboard that enables anoperator to enter parameters for the CT scan and to display thereconstructed image. A mass storage device 44 provides a means forstoring operating programs.

During data acquisition, the CT imaging system 10 functions as aconventional cone-beam system in gathering data. In the step-and-shootacquisition method, the table 39 is held stationary as the x-ray emitter12 and detector array 20 make a complete revolution around the gantry 24about the axis of rotation 26. At each of a plurality of angularpositions β, the attenuation data from all the detectors 22 in array 20are stored in the mass memory 44. Upon completion of a full rotation,the computer 38 commands the table motor control 37 to advance the table39 to another position along the z-axis 26 and another rotational scanof the patient 18 is performed. This process is repeated until thedesired portion of the patient 18 has been fully scanned. Alternatively,the CT imaging system 10 may acquire data in the helical acquisitionmode, wherein the table motor control 37 advances the table 39 as thex-ray emitter 12 and detector array 20 are rotated and scan data isbeing acquired.

FIG. 3 illustrates a Positron Emission Tomography (PET) scanner system100 formed in accordance with an embodiment of the present invention.The PET scanner system 100 includes an acquisition system 102, anoperator work station 104, acquisition, locator and coincidence (ALC)circuitry 106, and image reconstruction components 108.

The PET scanner system 100 includes a gantry 110 which supports adetector ring assembly 112 about a central bore which defines an imagingarea 114. A patient table (not illustrated) is positioned in front ofthe gantry 110 and is aligned with the imaging area 114. A patient tablecontroller (not shown) moves a table bed into the imaging area 114 inresponse to commands received from the operator work station 104 througha serial communications link 116.

A gantry controller 118 is mounted within the gantry 110 and isresponsive to commands received from the operator work station 104through the communication link 116 to operate the gantry 110. Forexample, the gantry 110 may perform a “coincidence timing calibrationscan” to acquire corrective data, or an “emission scan” in whichpositron annihilation events are counted.

FIG. 4 illustrates a detector unit 120 and associated PMT signals inaccordance with an embodiment of the present invention. The detectorring assembly 112 comprises a large number of detector units 120.Although not illustrated, detector units 120 are arranged in modules,each module including six separate and adjacent detector units 120. Atypical detector ring assembly 112 includes 56 separate modules suchthat each detector ring assembly 112 includes 336 separate detectorunits 120. Each detector unit 120 may include a set of bismuth germinate(BGO) scintillator crystals 122, such as crystals 124 and 126, arrangedin a 6×6 matrix and disposed in front of four photo multiplier tubes(PMTs) A, B, C and D which are collectively referred to by numeral 128.When a photon impacts a crystal 122, a scintillation event occurs andthe crystal generates light which is directed at PMTs 128. Each PMT 128receives at least some light generated by the scintillation event andproduces an analog signal 154-160 which arises sharply when ascintillation event occurs and then tails off exponentially with a timeconstant of approximately 300 nanoseconds. The relative magnitudes ofthe analog signals 154-160 are determined by the position in the 6×6 BGOmatrix at which a scintillation event takes place, and the totalmagnitude of these signals is determined by the energy of a photon whichcauses the event.

Returning to FIG. 3, a set of acquisition circuitry 130 is mountedwithin the gantry 110 to receive the four analog signals 154-160 fromeach detector unit 120 in the assembly 112. The acquisition circuitry130 provides analog signals 154-160 to ALC circuitry 106 via a data bus132. The ALC circuitry 106 uses the analog signals 154-160 to determinethe energy of a detected event, whether the energy detected likelycorresponds to a photon, the actual coordinates of a detected eventwithin the block of scintillation crystals 122, the time of the event(i.e. generates a time stamp) and compares event times to identifycoincidence pairs of events that are stored as coincidence data packets.Each coincidence data packet includes a pair of digital numbers whichprecisely identify the addresses of the two scintillation crystals 122that detected an associated event.

Image reconstruction components 108 includes a sorter 134, a memorymodule 136, one or more IG processors 138, a data decomposer 162, animage CPU 140 and a backplane bus 142 which conforms to the VMEstandards and links all other processor components together. In FIG. 3,two IG processors 138 are illustrated, although it should be understoodthat more or less than two IG processors 138 may be utilized by the PETscanner system 100. The primary purpose of sorter 134 is to generatememory addresses for the coincidence data packets to efficiently storecoincidence data. The set of all projection rays that point in the samedirection and pass through the PET scanner's FOV is a completeprojection, or “view”. A distance R between a particular projection rayand a center of the FOV locates that projection ray within the FOV. Asshown in FIG. 3, for example, a positron annihilation (hereinafter an“event”) 150 occurs along a projection ray 152 which is located in aview at the projection angle Θ and the distance R. The sorter 134 countsall of the events 150 which occur on this projection ray (R, Θ) duringan acquisition period by sorting out the coincidence data packets thatindicate an event at the two BGO detector crystals lying on theprojection ray 152.

During a data acquisition, the coincidence counts are organized inmemory 136 as a set of two-dimensional arrays, one for each axial image,and each having as one of its dimensions the projection angle Θ and theother dimension the distance R. The Θ by R array of detected events iscalled a histogram. Coincidence events occur at random and the sorter134 quickly determines the Θ and R values from the two crystal addressesin each coincidence data packet and increments the count of thecorresponding sinogram array element. At the completion of anacquisition period, memory 136 stores the total number of annihilationevents which occurred along each ray (R, Θ) in the histogram.

Image CPU 140 controls the backplane bus 142 and links the imagereconstruction components 108 to the communication link 116. The IGprocessors 138 also connect to the bus 142 and operate under thedirection of the image CPU 140. The data decomposer 162 sends image datato the IG processors 138 which process the raw image data, or thehistogram data, from the memory module 136 as discussed below. Theresulting image array may be stored in memory module 136 and is outputby the image CPU 140 to the operator work station 104.

The operator work station 104 includes a CPU 144, a CRT display 146 anda keyboard 148. The CPU 144 connects to the communications link 116 andscans the key board 148 for input information. Through the keyboard 148and associated control panel switches, an operator can controlcalibration of the PET scanner system 100, its configuration, and thepositioning of a patient table (not illustrated) during dataacquisition.

FIG. 5 illustrates a method for image reconstruction using datadecomposition in accordance with an embodiment of the present invention.FIG. 6 illustrates a data decomposition and image generation processmodel formed in accordance with an embodiment of the present invention.FIG. 6 comprises a data decomposer 250 and multiple IG processors252-256. The IG processors 252-256 are functionally identical, allowingthe architecture to be scaled to meet desired reconstruction performanceparameters. This redundancy improves the reconstruction reliability asthe system can meet functional requirements with as few as oneoperational IG processor 252-256. It should be understood that althoughonly three IG processors 252-256 are illustrated, more IG processors252-256 may be utilized. FIGS. 5 and 6 will be discussed together.

Turning to FIG. 5, in step 200, the acquisition system acquires rawimage data representative of an object of interest, such as the patient18. Acquisition systems such as the CT imaging system 10 and the PETscanner system 100 acquire raw image data as discussed previously. Forexample, the DAS 36 of the CT imaging system 10 has converted the rawimage data into digital form, while the histogram data from the PETscanner system 100 may be stored in the memory module 136. By way ofexample only, the image data may comprise enough information to form 900images or frames of data. It should be understood that it is notnecessary to acquire all of the image data prior to beginning thedecomposing and processing steps below. In addition, the method of FIG.5 and process model of FIG. 6 are not limited to CT and PET image data,but may be utilized by other diagnostic systems such as NuclearMedicine, Magnetic Resonance Imaging (MRI), Ultrasound, and the like.

In step 202, the raw image data is sent to a data decomposer 250. Thedata decomposer 250 may comprise hardware and/or software. Therefore,the data decomposer 250 may be a separate component, or may be includedwithin the computer 38 and mass storage 44 (FIG. 2) or the image CPU 140and memory 136 (FIG. 3). Therefore, the data decomposer 250 may be aprocess which is run by a separate processor, such as the computer 38 orimage CPU 140. By way of example only, the data decomposer 250 may be apart of a data acquisition and reconstruction control module (DARC) ofthe CT imaging system 10.

In step 204, the data decomposer 250 decomposes, or divides, the rawimage data. The raw image data may be decomposed into threesubstantially equal portions, or subsets of image data. Continuing theexample above wherein image data to form 900 data frames is acquired,each of three subsets of raw image data may comprise 300 data frames. Itshould be understood that the raw image data may not be decomposed basedon data frames, and that the example is for illustrative purposes only.The first subset may comprise the first 300 data frames, the secondsubset may comprise the next 300 data frames, while the third subset maycomprise the last 300 data frames. In another example, if the raw imagedata comprises data information for 1000 frames of data, the first andsecond subsets may comprise 333 data frames apiece, while the thirdsubset comprises 334 data frames. The data decomposer 250 decomposes theraw image data into a number N of subsets based on the number ofavailable IG processors 252-256. In other words, the data decomposer 250determines how many IG processors 252-256 are available andautomatically reconfigures itself to utilize the available IG processors252-256.

In step 206, the data decomposer 250 sends the subsets of raw image datato the IG processors 252-256. For example, the first subset of raw imagedata may be sent to the IG processor 252, the second subset of raw imagedata may be sent to the IG processor 254, and the third subset of rawimage data may be sent to the IG processor 256. The IG processors252-256 each receive the respective subset of raw image data and beginthe image generation process.

By way of example only, the data decomposer 250 may identify apredefined amount of raw image data for each IG processor 252-256.Therefore, once the image acquisition has been started and a portion ofthe raw image data has been acquired and received by the data decomposer250, a predefined amount of raw image data is sent to each IG processor252-256. The data decomposer 250 receives and holds raw image data as itis acquired. Then, when an IG processor 252-256 becomes available, thedata decomposer 250 sends an amount of raw image data to the availableIG processor 252-256. For example, the data decomposer 250 may send anamount of raw image data substantially equivalent to half of the totalraw image data currently acquired and waiting to be processed. When thenext IG processor 252-256 becomes available, the data decomposer 250 mayagain divide the raw image data currently acquired and waiting to beprocessed into two substantially equal subsets and send one subset tothe next available IG processor 252-256 for processing. In this manner,the IG processors 252-256 are each engaged with raw image data toprocess, and the time any one IG processor 252-256 is idle is minimized.

Alternatively, the data decomposer 250 may decompose the raw image datainto a number N of subsets where N is less than the number of total IGprocessors 252-256. For example, if raw image data from a previous scanremains to be processed, the data decomposer 250 may designate a portionof the total number of IG processors 252-256 to continue processing theprevious scan, and divide the raw image data from the current scanbetween the remaining IG processors 252-256.

The IG processors 252-256 reconstruct the raw image data based on areconstruction mode, which defines how the raw image data is processed.The reconstruction mode may be determined by the type of scan beingperformed, the anatomy being scanned, the desired image output, and thelike. Many different reconstruction modes exist, and each mode comprisesprocessing steps to be accomplished, optionally in a defined order,which may be different with respect to other reconstruction modes.Therefore, the steps 208-220 of FIG. 5 discussed below are illustrativeonly. It should be understood that different steps, a different numberof steps, and/or a different order of steps may be used in place ofsteps 208-220 to process the raw image data according to the desiredreconstruction mode. By way of example only, the reconstruction mode maybe an Iterative Bone Correction Algorithm used with the CT imagingsystem 10 or a CT attenuation correction used with the PET scannersystem 100. The reconstruction mode may be input by a user through theconsole 42.

The IG processors 252-256 each process data independently with respectto each other. Thus, the IG processors 252-256 do not interact with eachother, but rather operate in parallel. Also, as each of the IGprocessors 252-256 comprise the functional capability to process the rawimage data independently, it is only necessary to have one IG processor252-256. The IG processor 254 will be discussed below as an exemplary IGprocessor.

By way of example only, each of the IG processors 252-256 may comprise a“personal computer” or PC, having a motherboard, processors, and memory.Software processes are loaded in the memory and the processors processthe raw image data according to the software processes. Therefore, eachof the IG processors 252-256 may be a module, circuit board, or unitwhich is easily installed within the CT imaging system 10 and PETscanner system 100. As each of the IG processors 252-256 within animaging system are substantially the same, more IG processors 252-256may easily be added to the imaging system to increase the processingspeed and capability.

In step 208, a preprocess or data correction process submodule 258 ofthe IG processor 254 processes the raw image data. For example,preprocessing may comprise normalizing the raw image data by applyingcorrections based on calibration data and offset data particular to theCT imaging system. Alternatively, the PET scanner system 100 mayimplement a data correction process in which calibration data or otherknowledge of the PET scanner system 100 or patient 18 is used. Forexample, CT attenuation correction (CTAC) may be implemented for the PETscanner system 100, wherein CT images are used to obtain informationabout the patient's 18 anatomy and used to correct the image dataacquired by the PET scanner system 100.

In step 210, a view-weight submodule 260 of the IG processor 254processes the image data. An interpolation process is performed on viewsand rows to compensate for the scan acquisition mode (i.e. helical,cardiac for CT imaging system 10) wherein the desired weightingfunctions are based on the scan and reconstruction modes. Theview-weight submodule 260 may generate view-weighting weights based onview-weighting parameters and applies the weights to the image data.

In step 212, the rebinning submodule 262 of the IG processor 254performs rebinning of the image data. For example, the rebinningsubmodule 262 may perform an interpolation process on views and rowsacquired by the CT imaging system 10 to transform image data fromfan-beam data format to parallel-beam data format. Equal space rebinningmay be applied by the rebinning submodule 262 to process either CT orPET image data, and Fourier rebinning may be implemented by therebinning submodule 262 for PET image data to create a 2D data set froma 3D data set.

In step 214, a filter submodule 264 of the IG processor 254 filters theimage data. The selected reconstruction filter may be based on thereconstruction mode or input by the user. The filter submodule 264generates one or more Filters tables based on the Filter parameters andmay access a preselected protocol or a reconstruction filter input by auser to generate the Filters tables. The filter submodule 264 may applya mathematical filtration based on the Filters tables to the image dataon a view basis in response to the selected reconstruction filter.

In step 216, a back projection or iterative image reconstructionsubmodule 266 of the IG processor 254 processes the image data. Forexample, in the CT imaging system 10, back projection of view data andsummation into an image matrix may be performed. Alternatively, 2D or 3Diterative reconstruction methods for PET and CT may be implemented. Inthis case, one or more of the previous steps are optional, as filtering(step 214) is not required for iterative reconstruction techniques.

By way of example only, one or more additional submodules may beimplemented if the desired reconstruction mode is the Iterative BoneCorrection Algorithm. A reprojection submodule forms view data fromimage matrix data (step 216), then the iterative bone correctionalgorithm is applied.

In step 218, a post processing submodule 268 of the IG processor 254performs additional processing on the image data, such as ring fix andimage filters.

In step 220, the IG processor 254 sends the processed image data to thecomputer, such as computer 38 of FIG. 2. Similarly, the IG processors252 and 256 send processed image data to the computer 38. The processedimage data may be displayed and/or archived, for example. The datadecomposer 250 continuously monitors the status of each IG processor252-256. When an IG processor 252-256 becomes available, the datadecomposer 250 sends raw image data to the available IG processor252-256 to be processed as discussed previously.

Therefore, it should be understood that each of the IG processors252-256 comprise a copy of the entire image generation work flow whichruns in parallel across the multiple IG processors 252-256. Performanceis enhanced by reducing the image reconstruction times, and thus theoverall time required for CT and PET studies. By adding additional IGprocessors 252-256, additional and/or more complex image reconstructionalgorithms may be performed, improving image quality and/or quantitywithout negatively impacting the image reconstruction or exam times.Thus, the IG processors 252-256 provide improved performance,flexibility and reliability.

Alternatively, individual IG processors 252-256 may be directed, orconfigured via software, to support unique image generation processmodels, enabling simultaneous support for multiple image reconstructionmodes. The reconfiguration of the IG processors 252-256 via software maybe changed on the fly as the IG processors 252-256 are not dedicated toany particular image reconstruction mode.

For example, a first IG processor 252 may be directed to process rawimage data from a first scan and a second IG processor 254 may bedirected to process raw image data from a second scan wherein the IGprocessors 252 and 254 utilize different image generation processmodels. Alternatively, a first IG processor 252 may be directed toprocess raw image data using a first image reconstruction mode and asecond IG processor 254 may be directed to process the same raw imagedata using a second image reconstruction mode. A third IG processor 256may be directed to process raw image data in either of the first andsecond image reconstruction modes, operating in parallel with one of thefirst and second IG processors 254 and 256 on a subset of the same rawimage data, directed to process raw image data according to a thirdimage reconstruction mode, or to process raw image data from a differentscan.

In addition, image reconstruction corrections specific to individualrows of data or processing multiple rows of data at one time can now besupported. Previously, processing speed may have been improved byprocessing a single row of data, which was then combined with othersingle rows into an image. By way of comparison, the IG processors252-256 can process data from individual or multiple rows of data,providing more system flexibility and processing speed.

By utilizing the data decomposition model of FIG. 6, the system designof the image reconstruction process function is simplified, whichenables faster development and improved maintainability. Adding newunits to a diagnostic system is coordinated at a higher level comparedto previous systems. For example, an IG processor 252-256 which is notfunctioning properly can easily be replaced or removed with minimal orno impact to the user of the diagnostic system.

As discussed previously, the processing flow illustrated within the IGprocessors 252-256 in FIG. 6 and described in FIG. 5 is exemplary, andthe CT imaging system 10 and PET scanner system 100 utilize differentprocessing flows to achieve different image processing modes. Forexample, an iterative loop including a subset of one or more submodules258-268 may be needed. For example, for PET 3D iterative reconstructiona preprocessing step may be followed by an iterative reconstructionprocess having several different corrections.

Also, the image reconstruction processing flow may not utilize all ofthe submodules 258-268 described above. Some of the processing steps maybe optional and thus any subset of the processing steps, in combinationwith additional steps if necessary, forms a valid image reconstructionprocess. Therefore, the image reconstruction processing flow mayimplement the submodules 258-268 in a different order, in combinationwith other submodules not specifically mentioned above, or also byexcluding one or more of the submodules 258-268. For example,view-weight submodule 260 may be utilized only in specific applicationsfor the PET image data. Volume CT may be accomplished by a first processflow of rebinning, filtering, and view-weighting; a second process flowof view-weighting, rebinning, and filtering; or a third process flow offiltering, rebinning, and view-weighting.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

1. A method for generating images, comprising: acquiring raw image datarepresentative of an object of interest; decomposing said raw image datainto N subsets of raw image data, wherein said N being based on a numberof available image generation processors; and processing said N subsetsof raw image data to create processed image data, said image generationprocessors performing image processing on said image data in parallelwith respect to each other.
 2. The method of claim 1, said raw imagedata being acquired by an acquisition system, said acquisition systembeing one of computed tomography (CT), positron emission tomography(PET), Nuclear Medicine, Magnetic Resonance Imaging (MRI), andUltrasound.
 3. The method of claim 1, said processing step furthercomprising applying at least one of calibration data and offset data tosaid raw image data, said calibration and offset data being based onsystem correction data representative of an acquisition system used toacquire said raw image data.
 4. The method of claim 1, said processingstep further comprising: generating view weighting weights based on atleast one of a scan acquisition mode and a reconstruction process; andprocessing said subsets of raw image data by applying said viewweighting weights.
 5. The method of claim 1, wherein said number ofavailable said image generation processors being equal to N.
 6. Themethod of claim 1, wherein said number of available image generationprocessors being greater than said N.
 7. The method of claim 1, whereinsaid N subsets of raw image data comprising substantially equal amountsof said raw image data.
 8. The method of claim 1, said processing stepfurther comprising: processing said raw image data with a first saidimage generation processor according to a first reconstruction process;and processing said raw image data with a second said image generationprocessor according to a second reconstruction process, said first andsecond reconstruction processes being different.
 9. The method of claim1, further comprising: monitoring said image generation processors toidentify when each of said image generation processors has completedprocessing said raw image data; decomposing said raw image data into twosubsets of said raw image data; and sending one of said two subsets ofsaid raw image data to an available said image generation processor. 10.A method for increasing the performance of a system for processing rawimage data, comprising: acquiring raw image data representative of anobject of interest; dividing said raw image data into substantiallyequal subsets of raw image data; and processing at least one of saidsubstantially equal subsets of raw image data with an image generationprocessor.
 11. The method of claim 10, further comprising: adding atleast one additional image generation processor, said image generationprocessors processing said raw image data in parallel with respect toeach other; and said dividing step further comprising dividing said rawimage data based on a total number of available said image generationprocessors.
 12. The method of claim 10, further comprising adding atleast one additional image generation processor, said image generationprocessors processing said raw image data in parallel with respect toeach other and performing substantially equivalent processing on saidsubstantially equal subsets of raw image data.
 13. The method of claim10, further comprising: adding at least one additional image generationprocessor, said image generation processors processing said raw imagedata in parallel with respect to each other; dividing said imagegeneration processors into subsets of said image generation processors;and directing said subsets of said image generation processors toprocess said raw image data according to different image generationprocess models.
 14. The method of claim 10, said substantially equalsubsets of raw image data being a predefined amount of raw image data.15. A scalable apparatus for processing raw image data, comprising: adata decomposer for dividing raw image data acquired by a dataacquisition system; and at least two image generation processors forprocessing said raw image data in parallel with respect to each other.16. The apparatus of claim 15, said data decomposer further comprisingdividing said raw image data into N subsets of raw image data, wherein Nequals an available number of said at least two image generationprocessors.
 17. The apparatus of claim 15, wherein said at least twoimage generation processors being substantially identical.
 18. Theapparatus of claim 15, wherein said data decomposer identifying anavailable image generation processor, said data decomposer sending saidavailable image generation processor a portion of said raw image data.19. The apparatus of claim 15, wherein said image acquisition systemfurther comprising one of a positron emission tomography (PET)acquisition system for acquiring raw PET image data and a computedtomography (CT) acquisition system for acquiring raw CT image data, saidimage generation processors outputting processed image data based onsaid raw PET image data and said raw CT image data, respectively. 20.The apparatus of claim 15, wherein said at least two image generationprocessors performing substantially equivalent processing on said rawimage data.